Battery replacement optimal configuration and guiding method and system considering automatic driving of electric vehicle

Abstract

The invention relates to an electric vehicle automatic driving-based battery swap planning and optimal configuration and guiding method and system, and the method comprises the steps: firstly constructing a battery swap optimal configuration and guiding model which takes the minimization of the comprehensive battery swap cost of an automatic driving electric vehicle as a target and considers the operation constraint of a battery swap station and the battery swap constraint of the electric vehicle; and then solving the battery swap optimal configuration and guide model to obtain an electric vehicle battery swap station optimal configuration scheme and a vehicle battery swap guide scheme. According to the invention, on the premise of satisfying traffic constraints, the power conversion time cost of a user is effectively reduced, and the hardware configuration cost and the power purchase cost of a power conversion service provider are reduced while the access requirement of the power distribution network is satisfied.

Description

Technical Field

[0001] The present invention relates to a battery replacement planning and optimization guidance method, and in particular to a battery replacement optimization configuration and guidance method and system taking into account automatic driving of electric vehicles. Background Art

[0002] Electric vehicle battery swapping technology and autonomous driving technology have made significant progress in recent years and have become an important technical direction to promote future transportation changes. Battery swapping technology solves the problem of time-consuming traditional charging methods by quickly replacing battery packs at special battery swapping stations. This technology has obvious application advantages for high-frequency vehicles, which can greatly reduce the time consumed by vehicle energy replenishment and improve vehicle utilization. In addition, battery swapping technology can reduce the pressure on the power grid to a certain extent by charging batteries in a centralized manner during off-peak hours of the power grid, because batteries can be charged in a centralized manner during off-peak hours. However, the configuration cost of battery swapping facilities is high, and limited battery swapping facilities are still difficult to meet the battery swapping needs of a high proportion of electric vehicles in the future during peak hours.

[0003] The progress of autonomous driving technology is also eye-catching. The core is to enable vehicles to complete driving tasks autonomously in different scenarios through sensors, artificial intelligence algorithms and high-precision maps. With the continuous iteration of technology, the safety and stability of autonomous driving have been significantly improved. In addition to the daily commuting of vehicle users, the effective application of the advantages of autonomous electric vehicles to the field of vehicle battery replacement is also a direction that needs to be explored. Summary of the invention

[0004] The purpose of the present invention is to overcome the above-mentioned problems existing in the prior art and to provide a method and system for optimizing the configuration and guiding of battery replacement taking into account the automatic driving of electric vehicles.

[0005] To achieve the above objectives, the technical solution of the present invention is:

[0006] In a first aspect, the present invention proposes a battery replacement optimization configuration and guidance method considering automatic driving of electric vehicles, comprising:

[0007] S1. Constructing a battery swap optimization configuration and guidance model for the power-transportation coupling network, wherein the battery swap optimization configuration and guidance model aims to minimize the comprehensive battery swap cost of autonomous electric vehicles and considers the battery swap station operation constraints and electric vehicle battery swap constraints;

[0008] S2. Solve the battery swap optimization configuration and guidance model to obtain the electric vehicle battery swap station optimization configuration plan and vehicle battery swap guidance plan.

[0009] The objective function of the battery swap optimization configuration and guidance model includes:

[0010] min C H[(1+α) γ -1] / αγ 2 +C E +C T ;

[0011]

[0012] In the above formula, C H is the configuration cost of the battery swap station; α is the discount rate; γ is the investment period; C E The cost of purchasing electricity for the battery swap station; C T The time cost of battery replacement; is the number of battery swapping facilities in the battery swapping station at node n; PR S The configuration cost of a single battery swap facility; is the number of battery charging facilities in the battery swap station at node n; PR C is the configuration cost of a single battery charging facility; d is the number of typical days in a year, and t is the time period in a typical day; The active power output from the distribution network to the power swap station connected to node e during time period t; is the electricity purchase price in period t; PR is the total time consumed by battery replacement during the vehicle use period in time period t; T The unit time cost.

[0013] The operation constraints of the battery swap station include:

[0014]

[0015]

[0016] In the above formula, WB t,n WB is the number of fully charged batteries stored in the battery swap station at node n during time period t; M The upper limit of the number of fully charged batteries stored in the battery swap station; VB t,n VB is the number of batteries to be charged stored in the battery swap station at node n in time period t; M The upper limit of the number of batteries to be charged stored in the charging station; WB t-1,n is the number of fully charged batteries stored in the battery swap station at node n in time period t-1; CB t,n SB is the number of batteries that have completed charging in the battery swap station at node n during time period t; t,n is the number of batteries performing battery swapping operations in the battery swapping station at node n in time period t; WT t,n,m VB is the number of fully charged batteries transported from the battery swap station at node m to the battery swap station at node n during time period t; t-1,n is the number of batteries to be charged stored in the battery swap station at node n in time period t-1; VTt,n,m is the number of batteries to be charged transported from the battery swap station at node m to the battery swap station at node n during time period t; M is the large M constant; σ W and σ V are the flow direction decision variables for the batteries that have been charged and the batteries to be charged, respectively. When it is 1, the battery is transported from the battery swap station n to the battery swap station m. When it is 0, the battery is transported from the battery swap station m to the battery swap station n. is the number of battery charging facilities in the battery swap station at node n; is the number of battery swapping facilities in the battery swapping station at node n; is the binary planning decision variable of the battery swap station. When a battery swap station is built at node n, When , no battery swap station is built at node n; BS M is the maximum number of battery swap stations; BS M is the maximum number of battery swap stations; is the number of battery swapping facilities in the battery swapping station at node n; T U is the unit time; T US Benchmark time consumption for battery replacement service; is the number of battery charging facilities in the battery swap station at node n; T C The time it takes to charge a single battery.

[0017] The electric vehicle battery replacement behavior constraints include:

[0018]

[0019]

[0020] In the above formula, and are the driving time and battery replacement time of the battery replacement trip in the idle period starting at time period t, respectively; dm is the number of the battery replacement demand; kv and kj are the outbound path numbers of the battery replacement trip in the idle period and the vehicle use period; L t,kv is the total length of the path kv in time period t; is the congestion coefficient of time period t; v A is the average driving speed of autonomous electric vehicles; For the period The congestion coefficient; The return period of the battery replacement trip during the idle period; T US Benchmark time for battery replacement service; FV dm,t,kv The battery swap demand dm is the traffic flow generated by the battery swap solution during the idle period of the path kv in the outbound journey during the time period t; For the battery replacement demand dm, in the time period Adoption Path The traffic flow generated by the off-peak period battery replacement scheme on the return trip; is the return path number for the battery replacement trip during the idle period; τ V is a binary auxiliary variable; δ M is the large M constant; TS dm,t is the battery replacement demand dm, the trip type determination constant in time period t, when TS dm,t = 1, time period t is the vehicle usage period corresponding to the battery replacement demand dm. dm,t =0, time period t is the idle time period of the vehicle corresponding to the battery replacement demand dm; tx is the auxiliary time period variable; FJ is the total time consumed by battery replacement during the vehicle use period in time period t; dm,t,kj is the battery replacement demand dm, and the traffic flow generated by the battery replacement scheme during the vehicle usage period using path kj in time period t; SB t,n is the number of batteries performing battery swapping operations in the battery swapping station at node n in time period t; is the correlation coefficient between path kv and node n; is the correlation coefficient between path kj and node n; FD dm,t is the number of vehicles corresponding to the battery replacement demand dm in time period t.

[0021] The battery swap optimization configuration and guidance model also considers distribution network operation constraints and traffic demand constraints. The traffic demand constraints include:

[0022]

[0023] In the above formula, FT t,l is the total traffic flow on road l during period t; FV dm,t,kv The battery swap demand dm is the traffic flow generated by the battery swap solution during the idle period of the path kv in the outbound journey during the time period t; For the battery replacement demand dm, in the time period Adoption Path The traffic flow generated by the off-peak period battery replacement scheme on the return trip; is the correlation coefficient between path kv and road l; is the correlation coefficient between path kj and road l; FC l is the upper limit of traffic capacity of road l;

[0024] The distribution network operation constraints include:

[0025]

[0026] In the above formula, is the active power output by the distribution network to the battery swap station connected to node e during time period t; CB t,nis the number of batteries that have completed charging in the battery swap station at node n during time period t; E D The average charging energy requirement for a single battery; is the correlation coefficient between the transportation network node n and the distribution network node e; T U is the unit time; and are the active and reactive power on line w in time period t, respectively; w represents all lines connected to the distribution network node e; and are the basic active and reactive loads connected to the distribution network node e at time period t; LC w is the capacity of the distribution network line w; ΔU t,w is the voltage drop on the distribution network line w during time period t; and are the resistance and reactance of the distribution network line w respectively; U N is the rated voltage of the distribution network bus; U t,a and U t,b are the bus voltages of nodes a and b in the distribution network in time period t, and nodes a and b are the two endpoints of the distribution network line w; U m and U M are the upper and lower limits of the distribution network bus voltage respectively; U t,e is the bus voltage at node e in period t.

[0027] In a second aspect, the present invention proposes a battery swap optimization configuration and guidance system considering electric vehicle autonomous driving, including a model building module and a model solving module;

[0028] The model building module is used to build a battery swap optimization configuration and guidance model, which aims to minimize the comprehensive battery swap cost of the autonomous electric vehicle and takes into account the battery swap station operation constraints and the electric vehicle battery swap constraints;

[0029] The model solving module is used to solve the battery swap optimization configuration and guidance model to obtain the electric vehicle battery swap station optimization configuration plan and the vehicle battery swap guidance plan.

[0030] The objective function of the battery swap optimization configuration and guidance model includes:

[0031] min C H [(1+α) γ -1] / αγ 2 +C E +C T ;

[0032]

[0033] In the above formula, C His the configuration cost of the battery swap station; α is the discount rate; γ is the investment period; C E The cost of purchasing electricity for the battery swap station; C T The time cost of battery replacement; is the number of battery swapping facilities in the battery swapping station at node n; PR S The configuration cost of a single battery swap facility; is the number of battery charging facilities in the battery swap station at node n; PR C is the configuration cost of a single battery charging facility; d is the number of typical days in a year, and t is the time period in a typical day; The active power output from the distribution network to the power swap station connected to node e during time period t; is the electricity purchase price in period t; PR is the total time consumed by battery replacement during the vehicle use period in time period t; T The unit time cost.

[0034] The operation constraints of the battery swap station include:

[0035]

[0036]

[0037] In the above formula, WB t,n WB is the number of fully charged batteries stored in the battery swap station at node n during time period t; M The upper limit of the number of fully charged batteries stored in the battery swap station; VB t,n VB is the number of batteries to be charged stored in the battery swap station at node n in time period t; M The upper limit of the number of batteries to be charged stored in the charging station; WB t-1,n is the number of fully charged batteries stored in the battery swap station at node n in time period t-1; CB t,n SB is the number of batteries that have completed charging in the battery swap station at node n during time period t; t,n is the number of batteries performing battery swapping operations in the battery swapping station at node n in time period t; WT t,n,m VB is the number of fully charged batteries transported from the battery swap station at node m to the battery swap station at node n during time period t; t-1,n is the number of batteries to be charged stored in the battery swap station at node n in time period t-1; VT t,n,m is the number of batteries to be charged transported from the battery swap station at node m to the battery swap station at node n during time period t; M is the large M constant; σ W and σ Vare the flow direction decision variables for the batteries that have been charged and the batteries to be charged, respectively. When it is 1, the battery is transported from the battery swap station n to the battery swap station m. When it is 0, the battery is transported from the battery swap station m to the battery swap station n. is the number of battery charging facilities in the battery swap station at node n; is the number of battery swapping facilities in the battery swapping station at node n; is the binary planning decision variable of the battery swap station. When a battery swap station is built at node n, When , no battery swap station is built at node n; BS M is the maximum number of battery swap stations; BS M is the maximum number of battery swap stations; is the number of battery swapping facilities in the battery swapping station at node n; T U is the unit time; T US Benchmark time consumption for battery replacement service; is the number of battery charging facilities in the battery swap station at node n; T C The time it takes to charge a single battery.

[0038] The electric vehicle battery replacement behavior constraints include:

[0039]

[0040] In the above formula, and are the driving time and battery replacement time of the battery replacement trip in the idle period starting at time period t, respectively; dm is the number of the battery replacement demand; kv and kj are the outbound path numbers of the battery replacement trip in the idle period and the vehicle use period; L t,kv is the total length of the path kv in time period t; is the congestion coefficient of time period t; v A is the average driving speed of autonomous electric vehicles; For the period The congestion coefficient; The return period of the battery replacement trip during the idle period; T US Benchmark time for battery replacement service; FV dm,t,kv The battery swap demand dm is the traffic flow generated by the battery swap solution during the idle period of the path kv in the outbound journey during the time period t; For the battery replacement demand dm, in the time period Adoption Path The traffic flow generated by the off-peak period battery replacement scheme on the return trip; is the return path number for the battery replacement trip during the idle period; τ V is a binary auxiliary variable; δ M is the large M constant; TS dm,tis the battery replacement demand dm, the trip type determination constant in time period t, when TS dm,t = 1, time period t is the vehicle usage period corresponding to the battery replacement demand dm. dm,t =0, time period t is the idle time period of the vehicle corresponding to the battery replacement demand dm; tx is the auxiliary time period variable; FJ is the total time consumed by battery replacement during the vehicle use period in time period t; dm,t,kj is the battery replacement demand dm, and the traffic flow generated by the battery replacement scheme during the vehicle usage period using path kj in time period t; SB t,n is the number of batteries performing battery swapping operations in the battery swapping station at node n in time period t; is the correlation coefficient between path kv and node n; is the correlation coefficient between path kj and node n; FD dm,t is the number of vehicles corresponding to the battery replacement demand dm in time period t.

[0041] The battery swap optimization configuration and guidance model also considers distribution network operation constraints and traffic demand constraints. The traffic demand constraints include:

[0042]

[0043] In the above formula, FT t,l is the total traffic flow on road l during period t; FV dm,t,kv The battery swap demand dm is the traffic flow generated by the battery swap solution during the idle period of the path kv in the outbound journey during the time period t; For the battery replacement demand dm, in the time period Adoption Path The traffic flow generated by the off-peak period battery replacement scheme on the return trip; is the correlation coefficient between path kv and road l; is the correlation coefficient between path kj and road l; FC l is the upper limit of traffic capacity of road l;

[0044] The distribution network operation constraints include:

[0045]

[0046] In the above formula, is the active power output by the distribution network to the battery swap station connected to node e during time period t; CB t,n is the number of batteries that have completed charging in the battery swap station at node n during time period t; E D The average charging energy requirement for a single battery; is the correlation coefficient between the transportation network node n and the distribution network node e; T U is the unit time; and are the active and reactive power on line w in time period t, respectively; w represents all lines connected to the distribution network node e; and are the basic active and reactive loads connected to the distribution network node e at time period t; LC w is the capacity of the distribution network line w; ΔU t,w is the voltage drop on the distribution network line w during time period t; and are the resistance and reactance of the distribution network line w respectively; U N is the rated voltage of the distribution network bus; U t,a and U t,b are the bus voltages of nodes a and b in the distribution network in time period t, and nodes a and b are the two endpoints of the distribution network line w; U m and U M are the upper and lower limits of the distribution network bus voltage respectively; U t,e is the bus voltage at node e in period t.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] The battery swap optimization configuration and guidance model constructed by the battery swap optimization configuration and guidance method considering the automatic driving of electric vehicles proposed in the present invention, on the one hand, fully utilizes the advantages of self-driving electric vehicles, allowing them to go to the battery swap station for battery swap on their own during the user's non-use period, and greatly reduces the time cost due to battery swap operations under the premise of meeting traffic constraints; on the other hand, it utilizes the battery swap flexibility of the self-driving electric vehicles to optimize the battery swap flow, and reasonably plans the entry, exit and conversion of charged batteries and batteries to be charged in various time periods, further improving the load flexibility of the battery swap station, while meeting the distribution network access requirements, reducing the hardware configuration cost and electricity purchase cost of the battery swap service provider. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 This is a schematic diagram of the structure of the power-transportation coupling network in Example 1 of the present invention.

[0050] Figure 2 This is a time series characteristic curve of the congestion coefficient in Example 1 of the present invention.

[0051] Figure 3 It is the time-of-use electricity price in Example 1 of the present invention.

[0052] Figure 4 The present invention is a flowchart of the method.

[0053] Figure 5 Schematic diagram of the architecture and operation of the battery swap system in Example 1 of the present invention.

[0054] Figure 6This is a schematic diagram of the battery swap station configuration results in Example 1 of the present invention.

[0055] Figure 7 The structure diagram of the system of the present invention is shown in FIG. DETAILED DESCRIPTION

[0056] The present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0057] The present invention proposes a battery swap optimization configuration and guidance method taking into account the automatic driving of electric vehicles. The method utilizes the automatic driving electric vehicles to go to the battery swap station to perform the battery swap operation in the idle period without the intervention of the user, which saves the time cost of battery swap for the user, and improves the utilization rate of the equipment in the non-peak period for the battery swap station, reduces the queuing time and improves the flexibility of the operation of the battery swap station. By optimizing the configuration of battery swap and battery charging facilities and reasonably arranging the battery swap location and time of the automatic driving electric vehicles (i.e., the vehicle battery swap guidance plan), the hardware configuration cost, electricity purchase cost and time cost of the battery swap service provider due to the battery swap operation of the electric vehicle users can be reduced while meeting the distribution network access requirements and traffic constraints.

[0058] Embodiment 1:

[0059] In this embodiment, the topology structure is as follows Figure 1 The 11-node distribution network and 17-node transportation network shown in the figure are taken as the object (parameters: investment period is 10 years; discount rate is 0.05; unit time is 1 hour; investment period is 10 years; typical days in a year are 365 days; rated voltage of distribution network is 10 kV, upper and lower limits of distribution network bus voltage are 10.5 kV and 9.5 kV respectively; maximum power of single battery charging facility is 40 kW; average charging energy demand of single battery is 40 kWh; configuration cost of single battery charging facility is 20,000 yuan; cost of single electric vehicle battery replacement facility is 600,000 yuan; unit time cost is 20 yuan / hour; average driving speed of electric vehicle is 40 km / h when congestion coefficient is 1; upper limit of the number of battery replacement stations is 2; benchmark time consumption of battery replacement service is 0.3 hours; time consumption of single battery charging is 0.5 hours; congestion coefficient timing characteristic curve and time-of-use electricity price are as follows Figure 2 and Figure 3 As shown in the figure), implement the battery replacement optimization configuration and guidance method considering the electric vehicle automatic driving, such as Figure 4 As shown, the following steps are included:

[0060] 1. Build an optimized configuration and guidance model for battery swapping

[0061] The battery swap optimization configuration and guidance model regards the power-traffic coupling network as a whole, and its optimization goal is to minimize the comprehensive battery swap cost of autonomous electric vehicles. The comprehensive battery swap cost includes the configuration cost of the battery swap station facilities C H , Power purchase cost of battery swap station C E and the battery replacement time cost C T The schematic diagram of the architecture and operation mode of the battery swap system is as follows: Figure 5 As shown in the figure, the self-driving electric vehicle can go to the battery swap station to perform the battery swap operation when the user is not using the car, or go to the battery swap station to swap the battery during the user's trip. Since only the latter case will consume the user's time cost in the above two cases, when calculating the battery swap time cost, only the time cost caused by the battery swap during the trip is considered. The objective function of the model is shown in formula (1)-formula (4):

[0062] min C H [(1+α) γ -1] / αγ 2 +C E +C T Formula (1)

[0063]

[0064]

[0065] In the above formula, C H Cost of configuring the battery swap station facilities; C E The cost of purchasing electricity for the battery swap station; C T is the time cost of battery replacement; α is the discount rate; γ is the investment period; is the number of battery charging facilities in the battery swap station at node n; is the number of battery swapping facilities in the battery swapping station at node n; d is the number of typical days in a year, and t is the time period in a typical day; is the active power output of the distribution network to the power swap station connected to node e during time period t; PR S is the configuration cost of a single battery swap facility; PR C Cost of provisioning facilities for charging individual batteries; is the electricity purchase price in period t; PR T is the unit time cost; It is the total time consumed for battery replacement during the vehicle usage period within time period t.

[0066] The constraints of the model include:

[0067] (1) Operation constraints of battery swap stations

[0068] like Figure 5As shown, the battery swap station is equipped with battery swap facilities, battery charging facilities and batteries. The battery swap facilities are responsible for providing battery swap services for electric vehicles. The battery swap process consumes the fully charged batteries and generates batteries to be charged; the battery charging facilities are responsible for charging the batteries to be charged, thereby obtaining fully charged batteries. In order to ensure the flexibility of the battery swap service of the battery swap system including multiple battery swap stations, batteries are allowed to be transported between different battery swap stations. Therefore, the specific operation constraints of the battery swap station are set as follows:

[0069]

[0070]

[0071] In the above formula, WB t,n VB is the number of fully charged batteries stored in the battery swap station at node n during time period t; t,n WB is the number of batteries to be charged stored in the battery swap station at node n in time period t; t-1,n VB is the number of fully charged batteries stored in the battery swap station at node n in time period t-1; t-1,n WB is the number of batteries to be charged stored in the battery swap station at node n in time period t-1; M The upper limit of the number of fully charged batteries stored in the battery swap station; VB M The upper limit of the number of batteries to be charged that can be stored in the charging station; CB t,n SB is the number of batteries that have completed charging in the battery swap station at node n during time period t; t,n is the number of batteries performing battery swapping operations in the battery swapping station at node n in time period t; WT t,n,m is the number of fully charged batteries transported from the battery swap station at node m to the battery swap station at node n during time period t; VT t,n,m is the number of batteries to be charged transported from the battery swap station at node m to the battery swap station at node n during time period t; M is the large M constant; σ W and σ V are the flow direction decision variables for the batteries that have been charged and the batteries to be charged, respectively. When it is 1, the battery is transported from the battery swap station n to the battery swap station m. When it is 0, the battery is transported from the battery swap station m to the battery swap station n. is the number of battery charging facilities in the battery swap station at node n; is the number of battery swapping facilities in the battery swapping station at node n; is the number of battery charging facilities in the battery swap station at node n; is the number of battery swapping facilities in the battery swapping station at node n; T US The benchmark time for battery replacement service; T C The time consumed for a single battery charge; T U is the unit time; is the binary planning decision variable of the battery swap station. When a battery swap station is built at node n, When , no battery swap station is built at node n; BS M The maximum number of battery swap stations.

[0072] In this constraint, equations (5) and (6) are the upper limit constraints on the number of batteries in various states stored in the battery swap station; equations (7) and (8) are the battery flow constraints for electric vehicles; in order to avoid repeated transportation, in the same time period, only one-way transportation of batteries in the same state (completed charging or to be charged) is allowed between two battery swap stations; equations (9) and (10) are the battery flow constraints for the two states; equations (13) and (14) are the configuration constraints for battery swap facilities and battery charging facilities; equation (15) is the maximum number constraint for battery swap stations; equations (16) and (17) are the battery swap service capacity constraints and battery charging capacity constraints of the battery swap station.

[0073] (2) Restrictions on electric vehicle battery replacement

[0074] According to the user's use of electric vehicles, the time periods in a typical day can be divided into vehicle use periods and idle periods; when the electric vehicle performs battery replacement during the vehicle use period, the user drives the vehicle to the battery replacement station during the inherent vehicle use trip to replace the battery, which consumes the user's time and generates time costs; when the electric vehicle is charged during the idle period, the vehicle uses the automatic driving mode to go to the battery replacement station to replace the battery by itself, so that the user does not need to consume additional time costs. The specific settings of the electric vehicle battery replacement behavior constraints are as follows:

[0075]

[0076] In the above formula, dm is the number of the battery swapping demand; kv and kj are the outbound route numbers of the battery swapping trips during the idle period and the vehicle use period; The return period is for battery replacement during idle time; Number the return route for the battery replacement trip during the idle period; and are the driving time and battery replacement time of the idle period battery replacement trip starting at time period t, respectively; L t,kv is the total length of the path kv during time period t; FV dm,t,kv The battery swap demand dm is the traffic flow generated by the battery swap solution during the idle period of the path kv in the outbound journey during the time period t; For the battery replacement demand dm, in the time period Adoption Path The traffic flow generated by the off-peak period battery replacement scheme on the return trip; is the congestion coefficient of time period t; For the period The congestion coefficient of A is the average driving speed of the autonomous electric vehicle; T US Benchmark time for battery replacement service; TS dm,t is the battery replacement demand dm, the trip type determination constant in time period t, when TS dm,t = 1, time period t is the vehicle usage period corresponding to the battery replacement demand dm. dm,t =0, time period t is the idle time period of the vehicle corresponding to the battery replacement demand dm; tx is the auxiliary time period variable; τ V is a binary auxiliary variable; δ M is the large M constant; is the correlation coefficient between path kv and node n; is the correlation coefficient between path kj and node n; FJ is the total time consumed by battery replacement during the vehicle use period in time period t; dm,t,kj is the battery swap demand dm, and the traffic flow generated by the battery swap solution during the vehicle usage period using path kj in time period t; FD dm,t is the number of vehicles corresponding to the battery replacement demand dm in time period t.

[0077] In this constraint, equations (18) and (19) are the driving time and battery replacement operation time of battery replacement during the vehicle use period, respectively. Since the traffic demand and battery replacement demand during the vehicle use period are both concentrated, traffic jams and battery replacement queues may occur, resulting in increased time consumption. Equations (20) and (23) are constraints for battery replacement during the idle period, where equation (20) is the traffic constraint for the round trip of automatic driving battery replacement during the idle period; equation (21) is the time window constraint for automatic driving battery replacement during the idle period, that is, the vehicle must return before the end of the idle period; equations (22) and (23) are the traffic flow constraints generated by battery replacement during the idle period, that is, this type of traffic flow can only appear during the idle period. Equations (24) and (25) are constraints for battery replacement during the vehicle use period, where equation (24) is the traffic flow constraint generated by battery replacement during the vehicle use period, that is, this type of traffic flow can only appear during the vehicle use period; equation (25) is the relationship constraint between traffic flow and battery flow for battery replacement during the idle period. Formula (26) is the relationship constraint between traffic flow and battery flow for battery swapping in each period. Formula (27) is the battery swapping demand balance constraint.

[0078] (3) Traffic demand constraints

[0079]

[0080] In the above formula, FT t,l is the total traffic flow on road l during period t; FC l is the upper limit of traffic capacity of road l; is the correlation coefficient between path kv and road l; is the correlation coefficient between path kj and road l.

[0081] (4) Distribution network operation constraints

[0082]

[0083]

[0084] In the above formula, is the active power output by the distribution network to the battery swap station connected to node e during time period t; CB t,n is the number of batteries that have completed charging in the battery swap station at node n during time period t; E D The average charging energy requirement for a single battery; is the correlation coefficient between the transportation network node n and the distribution network node e; T U is the unit time; and are the active and reactive power on line w in time period t, respectively; w represents all lines connected to the distribution network node e; and are the basic active and reactive loads connected to the distribution network node e at time period t; LC w is the capacity of the distribution network line w; ΔU t,w is the voltage drop on the distribution network line w during time period t; and are the resistance and reactance of the distribution network line w respectively; U N is the rated voltage of the distribution network bus; U t,a and U t,b are the bus voltages of nodes a and b in the distribution network in time period t, and nodes a and b are the two endpoints of the distribution network line w; U m and U M are the upper and lower limits of the distribution network bus voltage respectively; U t,e is the bus voltage at node e in period t.

[0085] In this constraint, equation (30) is the battery charging power demand constraint of the electric vehicle battery swap station, equations (31) and (32) are the active and reactive power balance constraints of the distribution network nodes, respectively, equation (33) is the distribution network line capacity constraint, and equations (34)-(36) are the distribution network node voltage constraints.

[0086] 2. Solve the battery swap optimization configuration and guidance model to obtain the optimal configuration scheme of electric vehicle battery swap stations and the vehicle battery swap guidance scheme. The optimal configuration scheme of electric vehicle battery swap stations is as follows: Figure 5 As shown, the two battery swap stations are located at P2 (T2) and P6 (T8), with 5 and 8 battery swap facilities and 13 and 20 battery charging facilities respectively.

[0087] In order to verify the effectiveness of the method proposed in the present invention, the electric vehicle battery replacement optimization configuration and guidance method without taking into account the automatic driving mode is used as strategy 2 (no automatic driving mode is used to perform battery replacement, and all battery replacement needs are completed by the owner going to the battery replacement station during the journey) and applied to the 11-node distribution network-17-node transportation network. The economic efficiency is compared with the method proposed in the present invention (strategy 1). The results are shown in Table 1:

[0088] Table 1 Economic comparison

[0089]

[0090] By comparison, it can be seen that the annualized comprehensive charging cost is lower when strategy 1 is adopted; in terms of the annual configuration cost of the battery swap station facilities, the annual power purchase cost of the battery swap station is reduced by 22.38% when strategy 1 is adopted compared with strategy 2; in terms of the annual power swap time cost, the annual power swap time cost of strategy 1 is reduced by 41.53% compared with strategy 2; from the perspective of the annual comprehensive charging cost, the annual power swap time cost of strategy 1 is reduced by 16.00% compared with strategy 2. Therefore, the method proposed in the present invention achieves the effect of reducing the hardware configuration cost, power purchase cost of the battery swap service provider and the time cost of the electric vehicle user due to the battery swap operation while meeting the distribution network access requirements and traffic constraints.

[0091] Embodiment 2:

[0092] A battery swap optimization configuration and guidance system considering electric vehicle autonomous driving, including a model building module and a model solving module;

[0093] The model building module is used to build a battery swap optimization configuration and guidance model, and the objective function of the battery swap optimization configuration and guidance model is:

[0094] min C H [(1+α) γ -1] / αγ 2 +C E +C T ;

[0095]

[0096] In the above formula, C H is the configuration cost of the battery swap station; α is the discount rate; γ is the investment period; C E The cost of purchasing electricity for the battery swap station; C T The time cost of battery replacement; is the number of battery swapping facilities in the battery swapping station at node n; PR S The configuration cost of a single battery swap facility; is the number of battery charging facilities in the battery swap station at node n; PR C is the configuration cost of a single battery charging facility; d is the number of typical days in a year, and t is the time period in a typical day; The active power output from the distribution network to the power swap station connected to node e during time period t; is the electricity purchase price in period t; PR is the total time consumed by battery replacement during the vehicle use period in time period t; T is the unit time cost;

[0097] The constraints include:

[0098] The operation constraints of the battery swap station include:

[0099]

[0100] In the above formula, WB t,n WB is the number of fully charged batteries stored in the battery swap station at node n during time period t; M The upper limit of the number of fully charged batteries stored in the battery swap station; VB t,n VB is the number of batteries to be charged stored in the battery swap station at node n in time period t; M The upper limit of the number of batteries to be charged stored in the charging station; WB t-1,n is the number of fully charged batteries stored in the battery swap station at node n in time period t-1; CB t,n SB is the number of batteries that have completed charging in the battery swap station at node n during time period t; t,n is the number of batteries performing battery swapping operations in the battery swapping station at node n in time period t; WT t,n,m VB is the number of fully charged batteries transported from the battery swap station at node m to the battery swap station at node n during time period t; t-1,n is the number of batteries to be charged stored in the battery swap station at node n in time period t-1; VT t,n,m is the number of batteries to be charged transported from the battery swap station at node m to the battery swap station at node n during time period t; M is the large M constant; σ W and σ V are the flow direction decision variables for the batteries that have been charged and the batteries to be charged, respectively. When it is 1, the battery is transported from the battery swap station n to the battery swap station m. When it is 0, the battery is transported from the battery swap station m to the battery swap station n. is the number of battery charging facilities in the battery swap station at node n; is the number of battery swapping facilities in the battery swapping station at node n; is the binary planning decision variable of the battery swap station. When a battery swap station is built at node n, When , no battery swap station is built at node n; BS M is the maximum number of battery swap stations; BS M is the maximum number of battery swap stations; is the number of battery swapping facilities in the battery swapping station at node n; T U is the unit time; T US Benchmark time consumption for battery replacement service; is the number of battery charging facilities in the battery swap station at node n; T C The time it takes to charge a single battery.

[0101] The electric vehicle battery replacement behavior constraints include:

[0102]

[0103] In the above formula, and are the driving time and battery replacement time of the battery replacement trip in the idle period starting at time period t, respectively; dm is the number of the battery replacement demand; kv and kj are the outbound path numbers of the battery replacement trip in the idle period and the vehicle use period; L t,kv is the total length of the path kv in time period t; is the congestion coefficient of time period t; v A is the average driving speed of autonomous electric vehicles; For the period The congestion coefficient; The return period of the battery replacement trip during the idle period; T US Benchmark time for battery replacement service; FV dm,t,kv The battery swap demand dm is the traffic flow generated by the battery swap solution during the idle period of the path kv in the outbound journey during the time period t; For the battery replacement demand dm, in the time period Adoption Path The traffic flow generated by the off-peak period battery replacement scheme on the return trip; is the return path number for the battery replacement trip during the idle period; τ V is a binary auxiliary variable; δ M is the large M constant; TS dm,t is the battery replacement demand dm, the trip type determination constant in time period t, when TS dm,t = 1, time period t is the vehicle usage period corresponding to the battery replacement demand dm. dm,t =0, time period t is the idle time period of the vehicle corresponding to the battery replacement demand dm; tx is the auxiliary time period variable; FJ is the total time consumed by battery replacement during the vehicle use period in time period t; dm,t,kj is the battery replacement demand dm, and the traffic flow generated by the battery replacement scheme during the vehicle usage period using path kj in time period t; SB t,nis the number of batteries performing battery swapping operations in the battery swapping station at node n in time period t; is the correlation coefficient between path kv and node n; is the correlation coefficient between path kj and node n; FD dm,t is the number of vehicles corresponding to the battery replacement demand dm in time period t.

[0104] The battery swap optimization configuration and guidance model also considers distribution network operation constraints and traffic demand constraints. The traffic demand constraints include:

[0105]

[0106] In the above formula, FT t,l is the total traffic flow on road l during period t; FV dm,t,kv The battery swap demand dm is the traffic flow generated by the battery swap solution during the idle period of the path kv in the outbound journey during the time period t; For the battery replacement demand dm, in the time period Adoption Path The traffic flow generated by the off-peak period battery replacement scheme on the return trip; is the correlation coefficient between path kv and road l; is the correlation coefficient between path kj and road l; FC l is the upper limit of traffic capacity of road l;

[0107] The distribution network operation constraints include:

[0108]

[0109]

[0110] In the above formula, is the active power output by the distribution network to the battery swap station connected to node e during time period t; CB t,n is the number of batteries that have completed charging in the battery swap station at node n during time period t; E D The average charging energy requirement for a single battery; is the correlation coefficient between the transportation network node n and the distribution network node e; T U is the unit time; and are the active and reactive power on line w in time period t, respectively; w represents all lines connected to the distribution network node e; and are the basic active and reactive loads connected to the distribution network node e at time period t; LC w is the capacity of the distribution network line w; ΔU t,w is the voltage drop on the distribution network line w during time period t; and are the resistance and reactance of the distribution network line w respectively; U N is the rated voltage of the distribution network bus; U t,a and U t,b are the bus voltages of nodes a and b in the distribution network in time period t, and nodes a and b are the two endpoints of the distribution network line w; U m and U M are the upper and lower limits of the distribution network bus voltage respectively; U t,e is the bus voltage at node e in period t.

[0111] The model solving module is used to solve the battery swap optimization configuration and guidance model to obtain the electric vehicle battery swap station optimization configuration plan and the vehicle battery swap guidance plan.

Claims

1. A battery replacement optimization configuration and guidance method considering electric vehicle automatic driving, characterized in that: The method comprises: S1. Constructing a battery swap optimization configuration and guidance model for the power-transportation coupling network, wherein the battery swap optimization configuration and guidance model aims to minimize the comprehensive battery swap cost of autonomous electric vehicles and considers the battery swap station operation constraints and electric vehicle battery swap constraints; S2. Solve the battery swap optimization configuration and guidance model to obtain the electric vehicle battery swap station optimization configuration plan and vehicle battery swap guidance plan.

2. The battery replacement optimization configuration and guidance method considering automatic driving of electric vehicles according to claim 1, characterized in that: The objective function of the battery swap optimization configuration and guidance model includes: min C H [(1+a) γ -1] / ag 2 +C E +C T ; In the above formula, C H is the configuration cost of the battery swap station; α is the discount rate; γ is the investment period; C E The cost of purchasing electricity for the battery swap station; C T The time cost of battery replacement; is the number of battery swapping facilities in the battery swapping station at node n; PR S The configuration cost of a single battery swap facility; is the number of battery charging facilities in the battery swap station at node n; PR C is the configuration cost of a single battery charging facility; d is the number of typical days in a year, and t is the time period in a typical day; The active power output from the distribution network to the power swap station connected to node e during time period t; is the electricity purchase price in period t; PR is the total time consumed by battery replacement during the vehicle use period in time period t; T The unit time cost.

3. The battery replacement optimization configuration and guidance method considering automatic driving of electric vehicles according to claim 1, characterized in that: The operation constraints of the battery swap station include: In the above formula, WB t,n WB is the number of fully charged batteries stored in the battery swap station at node n during time period t; M The upper limit of the number of fully charged batteries stored in the battery swap station; VB t,n VB is the number of batteries to be charged stored in the battery swap station at node n in time period t; M The upper limit of the number of batteries to be charged stored in the charging station; WB t-1,n is the number of fully charged batteries stored in the battery swap station at node n in time period t-1; CB t,n SB is the number of batteries that have completed charging in the battery swap station at node n during time period t; t,n is the number of batteries performing battery swapping operations in the battery swapping station at node n in time period t; WT t,n,m VB is the number of fully charged batteries transported from the battery swap station at node m to the battery swap station at node n during time period t; t-1,n is the number of batteries to be charged stored in the battery swap station at node n in time period t-1; VT t,n,m is the number of batteries to be charged transported from the battery swap station at node m to the battery swap station at node n during time period t; M is the large M constant; σ W and σ V are the flow direction decision variables for the batteries that have been charged and the batteries to be charged, respectively. When it is 1, the battery is transported from the battery swap station n to the battery swap station m. When it is 0, the battery is transported from the battery swap station m to the battery swap station n. is the number of battery charging facilities in the battery swap station at node n; is the number of battery swapping facilities in the battery swapping station at node n; is the binary planning decision variable of the battery swap station. When a battery swap station is built at node n, When , no battery swap station is built at node n; BS M is the maximum number of battery swap stations; BS M is the maximum number of battery swap stations; is the number of battery swapping facilities in the battery swapping station at node n; T U is the unit time; T US Benchmark time consumption for battery replacement service; is the number of battery charging facilities in the battery swap station at node n; T C The time it takes to charge a single battery.

4. The battery replacement optimization configuration and guidance method considering automatic driving of electric vehicles according to claim 3 is characterized in that: The electric vehicle battery replacement behavior constraints include: In the above formula, and are the driving time and battery replacement time of the battery replacement trip in the idle period starting at time period t, respectively; dm is the number of the battery replacement demand; kv and kj are the outbound path numbers of the battery replacement trip in the idle period and the vehicle use period; L t,kv is the total length of the path kv in time period t; is the congestion coefficient of time period t; v A is the average driving speed of autonomous electric vehicles; For the period The congestion coefficient; The return period of the battery replacement trip during the idle period; T US Benchmark time for battery replacement service; FV dm,t,kv The battery swap demand dm is the traffic flow generated by the battery swap solution during the idle period of the path kv in the outbound journey during the time period t; For the battery replacement demand dm, in the time period Adoption Path The traffic flow generated by the off-peak period battery replacement scheme on the return trip; is the return path number for the battery replacement trip during the idle period; τ V is a binary auxiliary variable; δ M is the large M constant; TS dm,t is the battery replacement demand dm, the trip type determination constant in time period t, when TS dm,t = 1, time period t is the vehicle usage period corresponding to the battery replacement demand dm. dm,t =0, time period t is the idle time period of the vehicle corresponding to the battery replacement demand dm; tx is the auxiliary time period variable; FJ is the total time consumed by battery replacement during the vehicle use period in time period t; dm,t,kj is the battery replacement demand dm, and the traffic flow generated by the battery replacement scheme during the vehicle usage period using path kj in time period t; SB t,n is the number of batteries performing battery swapping operations in the battery swapping station at node n in time period t; is the correlation coefficient between path kv and node n; is the correlation coefficient between path kj and node n; FD dm,t is the number of vehicles corresponding to the battery replacement demand dm in time period t.

5. The battery replacement optimization configuration and guidance method considering automatic driving of electric vehicles according to claim 2 is characterized in that: The battery swap optimization configuration and guidance model also considers distribution network operation constraints and traffic demand constraints. The traffic demand constraints include: In the above formula, FT t,l is the total traffic flow on road l during period t; FV dm,t,kv The battery swap demand dm is the traffic flow generated by the battery swap solution during the idle period of the path kv in the outbound journey during the time period t; For the battery replacement demand dm, in the time period Adoption Path The traffic flow generated by the off-peak period battery replacement scheme on the return trip; is the correlation coefficient between path kv and road l; is the correlation coefficient between path kj and road l; FC l is the upper limit of traffic capacity of road l; The distribution network operation constraints include: In the above formula, is the active power output by the distribution network to the battery swap station connected to node e during time period t; CB t,n is the number of batteries that have completed charging in the battery swap station at node n during time period t; E D The average charging energy requirement for a single battery; is the correlation coefficient between the transportation network node n and the distribution network node e; T U is the unit time; and are the active and reactive power on line w in time period t, respectively; w represents all lines connected to the distribution network node e; and are the basic active and reactive loads connected to the distribution network node e at time period t; LC w is the capacity of the distribution network line w; ΔU t,w is the voltage drop on the distribution network line w during time period t; and are the resistance and reactance of the distribution network line w respectively; U N is the rated voltage of the distribution network bus; U t,a and U t,b are the bus voltages of nodes a and b in the distribution network in time period t, and nodes a and b are the two endpoints of the distribution network line w; U m and U M are the upper and lower limits of the distribution network bus voltage respectively; U t,e is the bus voltage at node e in period t.

6. A battery replacement optimization configuration and guidance system considering electric vehicle automatic driving, characterized in that: The system includes a model building module and a model solving module; The model building module is used to build a battery swap optimization configuration and guidance model, which aims to minimize the comprehensive battery swap cost of the autonomous electric vehicle and takes into account the battery swap station operation constraints and the electric vehicle battery swap constraints; The model solving module is used to solve the battery swap optimization configuration and guidance model to obtain the electric vehicle battery swap station optimization configuration plan and the vehicle battery swap guidance plan.

7. The battery replacement optimization configuration and guidance system considering the automatic driving of electric vehicles according to claim 6 is characterized in that: The objective function of the battery swap optimization configuration and guidance model includes: min C H [(1+a) γ -1] / ag 2 +C E +C T ; In the above formula, C H is the configuration cost of the battery swap station; α is the discount rate; γ is the investment period; C E The cost of purchasing electricity for the battery swap station; C T The time cost of battery replacement; is the number of battery swapping facilities in the battery swapping station at node n; PR S The configuration cost of a single battery swap facility; is the number of battery charging facilities in the battery swap station at node n; PR C is the configuration cost of a single battery charging facility; d is the number of typical days in a year, and t is the time period in a typical day; The active power output from the distribution network to the power swap station connected to node e during time period t; is the electricity purchase price in period t; PR is the total time consumed by battery replacement during the vehicle use period in time period t; T The unit time cost.

8. The battery replacement optimization configuration and guidance system considering the automatic driving of electric vehicles according to claim 7 is characterized in that: The operation constraints of the battery swap station include: In the above formula, WB t,n WB is the number of fully charged batteries stored in the battery swap station at node n during time period t; M The upper limit of the number of fully charged batteries stored in the battery swap station; VB t,n VB is the number of batteries to be charged stored in the battery swap station at node n in time period t; M The upper limit of the number of batteries to be charged stored in the charging station; WB t-1,n is the number of fully charged batteries stored in the battery swap station at node n in time period t-1; CB t,n SB is the number of batteries that have completed charging in the battery swap station at node n during time period t; t,n is the number of batteries performing battery swapping operations in the battery swapping station at node n in time period t; WT t,n,m VB is the number of fully charged batteries transported from the battery swap station at node m to the battery swap station at node n during time period t; t-1,n is the number of batteries to be charged stored in the battery swap station at node n in time period t-1; VT t,n,m is the number of batteries to be charged transported from the battery swap station at node m to the battery swap station at node n during time period t; M is the large M constant; σ W and σ V are the flow direction decision variables for the batteries that have been charged and the batteries to be charged, respectively. When it is 1, the battery is transported from the battery swap station n to the battery swap station m. When it is 0, the battery is transported from the battery swap station m to the battery swap station n. is the number of battery charging facilities in the battery swap station at node n; is the number of battery swapping facilities in the battery swapping station at node n; is the binary planning decision variable of the battery swap station. When a battery swap station is built at node n, When , no battery swap station is built at node n; BS M is the maximum number of battery swap stations; BS M is the maximum number of battery swap stations; is the number of battery swapping facilities in the battery swapping station at node n; T U is the unit time; T US Benchmark time consumption for battery replacement service; is the number of battery charging facilities in the battery swap station at node n; T C The time it takes to charge a single battery.

9. The battery replacement optimization configuration and guidance system considering automatic driving of electric vehicles according to claim 8, characterized in that: The electric vehicle battery replacement behavior constraints include: In the above formula, and are the driving time and battery replacement time of the battery replacement trip in the idle period starting at time period t, respectively; dm is the number of the battery replacement demand; kv and kj are the outbound path numbers of the battery replacement trip in the idle period and the vehicle use period; L t,kv is the total length of the path kv in time period t; is the congestion coefficient of time period t; v A is the average driving speed of autonomous electric vehicles; For the period The congestion coefficient; The return period of the battery replacement trip during the idle period; T US Benchmark time for battery replacement service; FV dm,t,kv The battery swap demand dm is the traffic flow generated by the battery swap solution during the idle period of the path kv in the outbound journey during the time period t; For the battery replacement demand dm, in the time period Adoption Path The traffic flow generated by the off-peak period battery replacement scheme on the return trip; is the return path number for the battery replacement trip during the idle period; τ V is a binary auxiliary variable; δ M is the large M constant; TS dm,t is the battery replacement demand dm, the trip type determination constant in time period t, when TS dm,t = 1, time period t is the vehicle usage period corresponding to the battery replacement demand dm. dm,t =0, time period t is the idle time period of the vehicle corresponding to the battery replacement demand dm; tx is the auxiliary time period variable; FJ is the total time consumed by battery replacement during the vehicle use period in time period t; dm,t,kj is the battery replacement demand dm, and the traffic flow generated by the battery replacement scheme during the vehicle usage period using path kj in time period t; SB t,n is the number of batteries performing battery swapping operations in the battery swapping station at node n in time period t; is the correlation coefficient between path kv and node n; is the correlation coefficient between path kj and node n; FD dm,t is the number of vehicles corresponding to the battery replacement demand dm in time period t.

10. The battery replacement optimization configuration and guidance system considering automatic driving of electric vehicles according to claim 7, characterized in that: The battery swap optimization configuration and guidance model also considers distribution network operation constraints and traffic demand constraints. The traffic demand constraints include: In the above formula, FT t,l is the total traffic flow on road l during period t; FV dm,t,kv The battery swap demand dm is the traffic flow generated by the battery swap solution during the idle period of the path kv in the outbound journey during the time period t; For the battery replacement demand dm, in the time period Adoption Path The traffic flow generated by the off-peak period battery replacement scheme on the return trip; is the correlation coefficient between path kv and road l; is the correlation coefficient between path kj and road l; FC l is the upper limit of traffic capacity of road l; The distribution network operation constraints include: In the above formula, is the active power output by the distribution network to the battery swap station connected to node e during time period t; CB t,n is the number of batteries that have completed charging in the battery swap station at node n during time period t; E D The average charging energy requirement for a single battery; is the correlation coefficient between the transportation network node n and the distribution network node e; T U is the unit time; and are the active and reactive power on line w in time period t, respectively; w represents all lines connected to the distribution network node e; and are the basic active and reactive loads connected to the distribution network node e at time period t; LC w is the capacity of the distribution network line w; ΔU t,w is the voltage drop on the distribution network line w during time period t; and are the resistance and reactance of the distribution network line w respectively; U N is the rated voltage of the distribution network bus; U t,a and U t,b are the bus voltages of nodes a and b in the distribution network in time period t, and nodes a and b are the two endpoints of the distribution network line w; U m and U M are the upper and lower limits of the distribution network bus voltage respectively; U t,e is the bus voltage at node e in period t.

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